SELF-LEARNING SATELLITE NAVIGATION ASSISTED HYBRID VEHICLE CONTROLS SYSTEM

• US 20110264317A1
• Filed: 04/23/2010
• Published: 10/27/2011
• Est. Priority Date: 04/23/2010
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations Incorporated (Motors Liquidation Co. GUC Trust)
• Inventors: Clingerman, Bruce J., Druenert, Volker, Pettit, William H.
• Granted Patent: US 8,374,740 B2
• Time in Patent Office: Days
• US Class Current: 701/22
• CPC Class Codes: B60K 6/48 Parallel type
• CPC Class Codes: B60L 2240/622 by satellite navigation
• CPC Class Codes: B60L 2240/70 Interactions with external data bases, e.g. traffic centres
• CPC Class Codes: B60L 2260/46 by self learning
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 3/10 Indicating wheel slip ; Correction of wheel slip
• CPC Class Codes: B60L 50/16 with provision for separate direct mechanical propulsion
• CPC Class Codes: B60L 50/40 using propulsion power supplied by capacitors
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B60L 7/14 for vehicles propelled by ac motors
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: B60W 10/08 including control of electric propulsion units, e.g. motors or generators
• CPC Class Codes: B60W 20/00 Control systems specially adapted for hybrid vehicles hybrid vehicle design, B60K6/00; electric vehicles B60L
• CPC Class Codes: B60W 20/11 using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance utilising navigation and traffic information in the control strategy B60W20/12
• CPC Class Codes: B60W 2540/043 Identity of occupants
• CPC Class Codes: B60W 2540/30 Driving style
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2710/244 Charge state
• CPC Class Codes: B60W 50/0097 : Predicting future conditions
• CPC Class Codes: Y02T 10/62 : Hybrid vehicles
• CPC Class Codes: Y02T 10/70 : Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 : Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 : Electric energy management in electromobility
• CPC Class Codes: Y02T 90/16 : Information or communication technologies improving the operation of electric vehicles

Abstract

A self-learning assisted hybrid vehicle system that includes a main power source for providing power to the vehicle, a supplemental power source for providing supplemental power for providing power to the vehicle and an electric motor or other mechanical system for driving the vehicle. The system also includes a self-learning controls unit that receives and stores information from a plurality of inputs associated with the vehicle. The self-learning controls unit uses the information to make predictions about future driving conditions of the vehicle to efficiently utilize the power sources of the hybrid vehicle.

Claims

1. A self-learning assisted hybrid vehicle system for a vehicle, said system comprising:

a GPS receiver for determining the location of the vehicle;

a main power source for providing main power for the vehicle;

a supplemental power source for providing supplemental power for the vehicle;

an electric motor receiving power from the supplemental power source and/or the main power source and driving the vehicle; and

a self-learning controls unit responsive to information from the GPS receiver and information from a plurality of inputs associated with the vehicle, said self-learning controls unit using the information to make predictions about future driving conditions of the vehicle to efficiently utilize the main and supplemental power sources, wherein the self-learning controls unit optimizes the power distribution between the main power source and the supplemental power source to reduce fuel consumption, reduce emissions and/or improve performance of the vehicle.

2. The system according to claim 1, wherein the plurality of inputs include speed and distance information from a speed/odometer input.

3. The system according to claim 1, wherein the plurality of inputs include information from a map database, commute data from a commute information database, and driver information from a driver identification system/database.

4. The system according to claim 1, wherein the plurality of inputs include time of day, day of week and calendar day information.

5. The system according to claim 1, wherein the plurality of inputs include vehicle load information from one or more vehicle load monitoring devices and infant/child seat information from infant/child seat monitoring devices.

6. The system according to claim 1, wherein the plurality of inputs include wheel slip information from one or more wheel slip monitoring devices and wind/weather conditions information from one or more wind/weather conditions monitoring devices.

7. The system according to claim 1, wherein the plurality of inputs include plug-in information from one or more plug-in monitoring devices and/or a plug-in database to predict when the vehicle will be plugged into a wall charger or similar external charging device.

8. The system according to claim 1, wherein the self-learning controls unit predicts future driving conditions to enable the vehicle to maximize the usable energy available from regenerative braking.

9. The system according to claim 1, further comprising an external hard-drive that is capable of sending and receiving information from the self-learning controls unit using the GPS receiver so as to enable a large amount of data from the vehicle to be collected and analyzed while minimizing the amount of nonvolatile random access memory required on the vehicle.

10. The system according to claim 1, wherein the supplemental power source is a DC battery, a flywheel or an ultracapacitor.

11. The system according to claim 1, wherein the main power source is a fuel cell system or an internal combustion engine.

12. A self-learning assisted hybrid vehicle system for a vehicle, said system comprising:

a GPS receiver for determining the location of the vehicle;

a main power source for providing main power for the vehicle, said main power source being an internal combustion engine or a fuel cell system;

a battery for providing supplemental power for the vehicle;

an electric motor or other mechanical system receiving power from the battery and driving the vehicle;

power electronics for conditioning power from the battery and regenerative braking energy that charges the battery; and

a self-learning controls unit that receives information from a plurality of inputs associated with the vehicle, said self-learning controls unit using the information to make predictions about future driving conditions of the vehicle to efficiently utilize the main and supplemental power sources so as to reduce fuel consumption, reduce emissions and/or improve performance of the vehicle.

13. The system according to claim 12, further comprising an external hard-drive that is capable of sending and receiving information from the self-learning controls unit using the GPS receiver so as to enable a large amount of data from the vehicle to be collected and analyzed while minimizing the amount of nonvolatile random access memory required on the vehicle.

14. The system according to claim 12, wherein the self-learning controls unit predicts future driving conditions to enable the vehicle to maximize the usable energy from regenerative braking.

15. The system according to claim 12, wherein the plurality of inputs include plug-in information from one or more plug-in monitoring devices and/or a plug-in database to predict when the vehicle will be plugged into a wall charger or similar external charging device.

16. The system according to claim 12, wherein the plurality of inputs include speed and distance information from a speed/odometer input.

17. The system according to claim 12, wherein the plurality of inputs include information from a map database, commute data from a commute information database, and driver information from a driver identification system/database.

18. The system according to claim 12, the plurality of inputs include time of day, day of week and calendar day information.

19. The system according to claim 12, wherein the plurality of inputs include vehicle load information from one or more vehicle load monitoring devices, infant/child seat information from infant/child seat monitoring devices, wheel slip information from one or more wheel slip monitoring devices, and wind/weather conditions information from one or more wind/weather conditions monitoring devices.

20. A method for a self-learning assisted operating method for a hybrid vehicle, said method comprising:

providing a GPS receiver for determining the location of the vehicle;

providing a main power source for powering the vehicle;

providing a supplemental power source for providing supplemental power for the vehicle

providing an electric motor or other mechanical system for driving the vehicle;

powering the electric motor and the main power source; and

providing a self-learning controls unit that receives information from the GPS receiver and information from a plurality of inputs associated with the vehicle, said self-learning controls unit using the information to make predictions about future driving conditions of the vehicle to efficiently utilize the power sources of the hybrid vehicle so as to reduce the fuel consumption, reduce emissions and/or improve performance of the hybrid vehicle.